Publications of Larry M. Manevitz

Publications of Larry Manevitz (Sorted by Forum)

To see the Publications of Larry Manevitz in Reverse Chronological Order look here.

Books

All the changes and corrections to Introduction to Model Theory and the Metamathematics of Algebra by A. Robinson for the new editionby North-Holland were made by me (at Robinson’s request)just prior to Robinson’s death. (284 pages.) (I am acknowledged in the preface.)

L. Manevitz (editor and reviewer). “Logic for Computer Science” by Yoram Hirshfeld of Tel Aviv University. I am one of the main editors and reviewers of this new Open University Book. (As is customary with the Open University I will be acknowledged in the book.)

Hummel, R. and Manevitz, L. A Statistical Approach to the Representation of Uncertainty in Beliefs Using Spread of Opinions, IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, Vol. 26 No. 3 378-384,1996.[link,PDF,PS]

Manevitz, L. and Givoli, D. Soft Computing and the FEM, World Online Symposium on Soft Computing 3, (WSC3), Japan, 1998. On the world wide web: http://www/bioele.nagoya-u.ac.jp/wsc3/ , 1998. This is also republished in Book Chapter E4 [link, PDF, PS]

Hananel Hazan and Larry Manevitz, The Liquid State Machine is Not Robust to Problems in Its Components, Computational Cognitive Neuroscience,CCNC-2009 Boston, accepted, to appear. Note: This is an expansion of No. 43. above. [link, PDF, PS]

Hazan H. and Manevitz L. (2010). The Liquid State Machine Is Not Robust To Problems In Its Components But Topological Constraints Can Restore Robustness. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation, pages 258-264. DOI: 10.5220/0003058902580264 [link, PDF, PS]

Manevitz, L. (Abstract) How Does Time Emerge from Structure? Two Examples from Neurophysiology, in Abstract Proceedings of Workshop on Computational/Mathematical Problems Arising from Neurophysiology, Haifa, January, 2001. [link, PDF, PS]

Editor of the Abstract Proceedings of the Workshop on Computational/Mathematical Problems (and Solutions?) Arising from Neurophysiology. This is the abstracts of the talks presented at the above meeting, Haifa, 2001. Published by Caesarea Rothschild Institute for Interdisciplinary Computer Science, (35 pages).

Co-editor of the Proceedings of the International Meeting: “Brain and Behavior: What is learning in the neural system?” This meeting took place at Caesarea Rothschild Institute under the auspices of the HIACS and Brain and Behavior Research Centers. (32 pages).

The focus is on both the development and application of abstractions of cognitive structures from the computational and mathematical viewpoint. This includes, on the one hand using techniques of machine learning and neurocomputation for pattern identification (note the work on fMRI identification F22, F23, F28 and text processing D18, D21, D23), cognitive modeling (note the work on reading D26 and memory D15), and brain modeling (note the work on the development of the cortex model D25 and an abstraction of rate selection D19).

The applied aspect is also of interest. See for example, the work on user modeling via neural networks F36 F8, applications to the Finite Element Method D8 D16 D20 D22,and applications to patient treatment and diagnosis in virtual reality environments F33..

My approach in all of this is unified, in the sense that I try to look at these items from the computational viewpoint. Nonetheless, the work is very interdisciplinary.

Current projects underway in my laboratory include: (i) feature selection appropriate for one-class fMRI classification tasks (ii) development and applications of a general cortical modeling tool that uses discrete integrate and fire neurons (iii) applications of neural network technology for user modeling in (a) virtual reality environment (b) museum visitor (iv) computational modeling of left and right hemisphere interaction in reading cognitive tasks (v) computational modeling of the hippocampus memory system (vi) machine learning applications to gene classification (vii) developing a data driven model free BOLD response curves for MRI analysis. Much of this work is highly interdisciplinary and collaborative.

In addition, I am looking at some theoretical issues in developing tools for temporal pattern recognition.